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210123 ||| eng |
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|a 9780128156100
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|a QA76.9.B45
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|a Berman, Jules J.
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|a Principles and practice of big data
|b preparing, sharing, and analyzing complex information
|c Jules J. Berman
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| 250 |
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|a Second edition
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| 260 |
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|a London
|b Academic Press
|c 2018
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| 300 |
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|a 1 online resource
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|a Chapter 2: Providing Structure to Unstructured Data; Section 2.1. Nearly All Data Is Unstructured and Unusable in Its Raw Form; Section 2.2. Concordances; Section 2.3. Term Extraction; Section 2.4. Indexing; Section 2.5. Autocoding; Section 2.6. Case Study: Instantly Finding the Precise Location of Any Atom in the Universe (Some Assembly Required); Section 2.7. Case Study (Advanced): A Complete Autocoder (in 12 Lines of Python Code); Section 2.8. Case Study: Concordances as Transformations of Text; Section 2.9. Case Study (Advanced): Burrows Wheeler Transform (BWT); Glossary; References
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|a Front Cover; Principles and Practice of Big Data: Preparing, sharing, and analyzing complex information; Copyright; Other Books by Jules J. Berman; Dedication; Contents; About the Author; Author's Preface to Second Edition; Author's Preface to First Edition; References; Chapter 1: Introduction; Section 1.1. Definition of Big Data; Section 1.2. Big Data Versus Small Data; Section 1.3. Whence Comest Big Data?; Section 1.4. The Most Common Purpose of Big Data Is to Produce Small Data; Section 1.5. Big Data Sits at the Center of the Research Universe; Glossary; References
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|a Includes bibliographical references and index
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|a Introduction -- Providing structure to unstructured data -- Identification, deidentification, and reidentification -- Metadata, semantics, and triples -- Classifications and ontologies -- Introspection -- Standards and data integration -- Immutability and immortality -- Assessing the adequacy of a big data resource -- Measurement -- Indispensable tips for fast and simple big data analysis -- Finding the clues in large collections of data -- Using random numbers to knock your big data analytic problems down to size -- Special considerations in big data analysis -- Big data failures and how to avoid (some of) them -- Data reanalysis : much more important than analysis -- Repurposing big data -- Data sharing and data security -- Legalities -- Societal issues
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|a Section 4.1. Metadata; Section 4.2. eXtensible Markup Language; Section 4.3. Semantics and Triples; Section 4.4. Namespaces; Section 4.5. Case Study: A Syntax for Triples; Section 4.6. Case Study: Dublin Core; Glossary; References; Chapter 5: Classifications and Ontologies; Section 5.1. It's All About Object Relationships; Section 5.2. Classifications, the Simplest of Ontologies; Section 5.3. Ontologies, Classes With Multiple Parents; Section 5.4. Choosing a Class Model; Section 5.5. Class Blending; Section 5.6. Common Pitfalls in Ontology Development
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|a Section 5.7. Case Study: An Upper Level Ontology; Section 5.8. Case Study (Advanced): Paradoxes; Section 5.9. Case Study (Advanced): RDF Schemas and Class Properties; Section 5.10. Case Study (Advanced): Visualizing Class Relationships; Glossary; References; Chapter 6: Introspection; Section 6.1. Knowledge of Self; Section 6.2. Data Objects: The Essential Ingredient of Every Big Data Collection; Section 6.3. How Big Data Uses Introspection; Section 6.4. Case Study: Time Stamping Data; Section 6.5. Case Study: A Visit to the TripleStore
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|a Chapter 3: Identification, Deidentification, and Reidentification; Section 3.1. What Are Identifiers?; Section 3.2. Difference Between an Identifier and an Identifier System; Section 3.3. Generating Unique Identifiers; Section 3.4. Really Bad Identifier Methods; Section 3.5. Registering Unique Object Identifiers; Section 3.6. Deidentification and Reidentification; Section 3.7. Case Study: Data Scrubbing; Section 3.8. Case Study (Advanced): Identifiers in Image Headers; Section 3.9. Case Study: One-Way Hashes; Glossary; References; Chapter 4: Metadata, Semantics, and Triples
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| 653 |
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|a Big data / fast
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|a Big data / http://id.loc.gov/authorities/subjects/sh2012003227
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|a COMPUTERS / Databases / Data Mining / bisacsh
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| 653 |
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|a Données volumineuses
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| 041 |
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|a eng
|2 ISO 639-2
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| 989 |
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|b OREILLY
|a O'Reilly
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| 015 |
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|a GBB8F7064
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| 776 |
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|z 0128156104
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| 776 |
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|z 9780128156094
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| 776 |
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|z 9780128156100
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| 776 |
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|z 0128156090
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| 856 |
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|u https://learning.oreilly.com/library/view/~/9780128156100/?ar
|x Verlag
|3 Volltext
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| 082 |
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|a 005.7
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| 520 |
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|a Principles and Practice of Big Data: Preparing, Sharing, and Analyzing Complex Information, Second Edition updates and expands on the first edition, bringing a set of techniques and algorithms that are tailored to Big Data projects. The book stresses the point that most data analyses conducted on large, complex data sets can be achieved without the use of specialized suites of software (e.g., Hadoop), and without expensive hardware (e.g., supercomputers). The core of every algorithm described in the book can be implemented in a few lines of code using just about any popular programming language (Python snippets are provided). Through the use of new multiple examples, this edition demonstrates that if we understand our data, and if we know how to ask the right questions, we can learn a great deal from large and complex data collections. The book will assist students and professionals from all scientific backgrounds who are interested in stepping outside the traditional boundaries of their chosen academic disciplines
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| 520 |
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|a Bringing a set of techniques and algorithms that are tailored to Big Data projects, this book offers case studies across a range of scientific and engineering disciplines and provides insights into semantics, identification, de-identification, vulnerabilities and regulatory/legal issues. --
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